Current drug discovery efforts have primarily focused on identifying agents that tackle specific preselected cellular targets. However, in many cases, a single drug does not correct all of the aberrantly functioning pathways in a disease to produce an effective treatment. Drugs directed at an individual target often have limited efficacy and poor safety profiles due to various factors, including compensatory changes in cellular networks upon drug stimulation, redundancy, crosstalk, and off-target activities. The use of drug combinations that act on multiple targets has been shown to be a more effective treatment strategy.
While a drug combination can be effective, developing optimized drug combinations for clinical trials can be extremely challenging. For example, even a small number of different drugs (six drugs) each tested at a few concentrations (seven dosages) results in 76=117,649 combinations. Screening all 117,649 combinations through in vitro tests for the most desirable combination is an enormous task in terms of labor and time. Also, a drug combination being effective in vitro does not always indicate that the same drug combination would be effective in vivo. Traditionally, when a drug combination is successfully validated in vitro, the combination is applied in vivo, either by keeping the same dosage ratios or by adjusting the drug administration to achieve the same drug blood levels as attained in vitro. This approach can suffer from absorption, distribution, metabolism, and excretion (ADME) issues. ADME describes the disposition of a pharmaceutical compound within an organism, and the four characteristics of ADME can influence the drug levels, kinetics, and, therefore, efficacy of a drug combination. The discontinuity from cell line to animal and from animal to human as a result of ADME poses a major barrier to efficiently identifying optimized drug combinations for clinical trials.
It is against this background that a need arose to develop the combinatorial optimization technique described herein.